search for: bods

The idea is that one wants to write the line of code below in a general way which works the same whether you specify ix as one column or multiple columns but the naming entirely changes when you do this and BOD[, 1] and transform(BOD, X=..., Y=...) or other hard coding solutions still require writing multiple cases. ix <- 1:2 transform(BOD, X = BOD[ix] * seq(6)) On Tue, Jul 24, 2018 at

Note the inconsistency in the names in these two examples. X.Time in the first case and Time.1 in the second case. > transform(BOD, X = BOD[1:2] * seq(6)) Time demand X.Time X.demand 1 1 8.3 1 8.3 2 2 10.3 4 20.6 3 3 19.0 9 57.0 4 4 16.0 16 64.0 5 5 15.6 25 78.0 6 7 19.8 42 118.8 >

I don't think it has much to do with transform in particular: > BOD <- data.frame(Time = 1:6, demand = runif(6)) > BOD[["X"]] <- BOD[1:2] * seq(6); BOD Time demand X.Time X.demand 1 1 0.8649628 1 0.8649628 2 2 0.5895380 4 1.1790761 3 3 0.6854635 9 2.0563906 4 4 0.4255801 16 1.7023206 5 5 0.5738793 25 2.8693967 6 6 0.9996713

I am just starting to code in R and need some help as I am used to doing this in SAS. I have a dataset that looks like this: Chemical Well1 Well2 Well3 Well4 BOD 13.2 14.2 15.5 14.2 O2 7.8 2.6 3.5 2.4 TURB 10.2 14.6 18.5 17.3 and so on with more chemicals.... I would like to transpose my data so that it looks like this: Chemical WellID Value BOD Well1 13.2 BOD Well2 14.2 BOD Well3 15.5 BOD

I think you meant to call BOD[,1] From ?transform, the ... arguments are supposed to be vectors, and BOD[1] is still a data.frame (with one column). So I don't think it's surprising transform gets confused by which name to use (X, or Time?), and kind of compromises on the name "Time". It's also in a note in ?transform: "If some of the values are not vectors of the

Is this not supposed to work? > dput(BOD, file = "/BOD.R") > source("/BOD.R") Error in attributes(.Data) <- c(attributes(.Data), attrib) : row names must be 'character' or 'integer', not 'double' > dput(iris, file = "/iris.R") > source("/iris.R") Error in attributes(.Data) <- c(attributes(.Data), attrib) :

Am 27.08.24 um 11:55 schrieb peter dalgaard: > Yes. A quirk, rather than a bug I'd say. One issue is that the internal logic of transform() relies on > > e <- eval(substitute(list(...)), `_data`, parent.frame()) > tags <- names(e) > > so untagged entries in ... will not be included. ... unless at least one is tagged: R> transform(BOD, 0:5, 1:6) Time

One oddity in transform that I recently noticed. It seems that to include a one-column data frame in the arguments one must name it even though the name is ignored. If the data frame has more than one column then it must also be named but in that case it is not ignored and the names are made up of a combination of that name and the data frame's names. I would have thought that if we did not

Hi list, I'm sure the explanation must be laughably simple to the experts out there, but I just could figure it out. I have a simple data frame that looks like, >head(da.off) DDate OffP 1 2005-01-01 41.23 2 2005-01-02 44.86 3 2005-01-03 44.86 4 2005-01-04 43.01 5 2005-01-05 45.47 6 2005-01-06 48.62 where the first column DDate currently is character, and OffP is numeric. I want

Dear All, Is sqldf equipped with delete queries? I have tried delete queries but with no success. Thanks in advance, Paul

Hi All, I'm trying to run nls on the data from the study by Marske (Biochemical Oxygen Demand Interpretation Using Sum of Squares Surface. M.S. thesis, University of Wisconsin, Madison, 1967) and was reported in Bates and Watts (1988). Data is as follows, (stored as mydata) time bod 1 1 0.47 2 2 0.74 3 3 1.17 4 4 1.42 5 5 1.60 6 7 1.84 7 9 2.19 8 11 2.17 I then

Hello All, I am trying to write a function which would operate on columns of a dataframe specified in parameters passed to that function. f = function(dataf, col1 = "column1", col2 = "column2") { dataf$col1 = dataf$col2 # just as an example } The above, of course, does not work as intended. In some languages one can force evaluation of a variable, and then

I ran a function called BoxCox, taken from the book by Venables and Ripley, for checking the need for power transformation. This function works fine using the version 0.50 of R, but gives an error message with version 0.60. The lm function in version 0.60 is different from that in version 0.50. Is there a bug in the new lm function? Kung-Sik Chan >

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

I would like to write a function that computes Tukey's 1 df for nonadditivity. Here is a simplified version of the function I'd like to write: (m is an object created by lm): tukey.test <- function(m) { m1 <- update(m, ~.+I(predict(m)^2)) summary(m1)$coef } The t-test for the added variable is Tukey's test. This won't work: data(BOD) m1 <- lm(demand~Time,BOD)

Hello, Using the RpgSQL package, there must be a way to get the row names into the table automatically. In the example below, I'm trying to get rid of the cbind line, yet have the row names of the data frame populate a column. > bentest = matrix(1:4,2,2) > dimnames(bentest) = list(c('ra','rb'),c('ca','cb')) > bentest ca cb ra 1 3 rb 2 4 >

Hello, When I do: dbWriteTable(con, "r.BOD", cbind(row_names = rownames(BOD), BOD)) ...can I specify the data types such as varchar(12), float, double precision, etc. for each of the fields/columns? If not, what is the best way to create a table with specified field data types (with the RpgSQL package/R)? Regards, Ben [[alternative HTML version deleted]]

Hello, I'm using package RpgSQL. Is there a better way to create a multi-line query/character string? I'm looking for less to type and readability. This is not very readable for large queries: s <- 'create table r.BOD("id" int primary key,"name" varchar(12))' I write a lot of code, so I'm looking to type less than this, but it is more readable from

Hi, I post this message to the general r-help list hoping anyone within a wider range have suggestions: There are three ways to integration R and postgres, especially on 64bit Microsoft windows Platform, 1. via RODBC package, which has 32 bit and 64 bit version for windows 2. via RPostgres interface, which only has 32bit version currently 3. via plr for Greenplum, which only supports a

The advantages of SQL are that - it can be used from many languages so if you know SQL you can easily move that part of your code to python, say, and visa versa - it is widely used - it can handle data stored outside of R and possibly otherwise too large for R - some SQL databases support multiple concurrent users - depending on the database it may be used to communicate the data to others - one